Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations750
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory64.6 KiB
Average record size in memory88.2 B

Variable types

Numeric6
Categorical5

Alerts

HiringDecision is highly overall correlated with RecruitmentStrategyHigh correlation
RecruitmentStrategy is highly overall correlated with HiringDecisionHigh correlation
DistanceFromCompany has unique values Unique
ExperienceYears has 50 (6.7%) zeros Zeros
SkillScore has 8 (1.1%) zeros Zeros

Reproduction

Analysis started2024-11-25 21:46:46.970358
Analysis finished2024-11-25 21:46:51.924609
Duration4.95 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct31
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.398667
Minimum20
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-11-25T18:46:52.012924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21
Q127
median36
Q344
95-th percentile49
Maximum50
Range30
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.2881335
Coefficient of variation (CV)0.26238654
Kurtosis-1.260901
Mean35.398667
Median Absolute Deviation (MAD)8
Skewness-0.076004138
Sum26549
Variance86.269424
MonotonicityNot monotonic
2024-11-25T18:46:52.150595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
49 37
 
4.9%
20 35
 
4.7%
22 31
 
4.1%
47 31
 
4.1%
42 31
 
4.1%
45 29
 
3.9%
44 29
 
3.9%
36 28
 
3.7%
24 27
 
3.6%
40 27
 
3.6%
Other values (21) 445
59.3%
ValueCountFrequency (%)
20 35
4.7%
21 18
2.4%
22 31
4.1%
23 19
2.5%
24 27
3.6%
25 22
2.9%
26 16
2.1%
27 24
3.2%
28 24
3.2%
29 23
3.1%
ValueCountFrequency (%)
50 24
3.2%
49 37
4.9%
48 27
3.6%
47 31
4.1%
46 19
2.5%
45 29
3.9%
44 29
3.9%
43 16
2.1%
42 31
4.1%
41 24
3.2%

Gender
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
1
376 
0
374 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters750
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 376
50.1%
0 374
49.9%

Length

2024-11-25T18:46:52.288520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T18:46:52.420750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 376
50.1%
0 374
49.9%

Most occurring characters

ValueCountFrequency (%)
1 376
50.1%
0 374
49.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 376
50.1%
0 374
49.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 376
50.1%
0 374
49.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 376
50.1%
0 374
49.9%

EducationLevel
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2
381 
3
153 
1
145 
4
71 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters750
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row4
5th row3

Common Values

ValueCountFrequency (%)
2 381
50.8%
3 153
20.4%
1 145
 
19.3%
4 71
 
9.5%

Length

2024-11-25T18:46:52.539622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T18:46:52.664229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 381
50.8%
3 153
20.4%
1 145
 
19.3%
4 71
 
9.5%

Most occurring characters

ValueCountFrequency (%)
2 381
50.8%
3 153
20.4%
1 145
 
19.3%
4 71
 
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 381
50.8%
3 153
20.4%
1 145
 
19.3%
4 71
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 381
50.8%
3 153
20.4%
1 145
 
19.3%
4 71
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 381
50.8%
3 153
20.4%
1 145
 
19.3%
4 71
 
9.5%

ExperienceYears
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7493333
Minimum0
Maximum15
Zeros50
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-11-25T18:46:52.783456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median8
Q312
95-th percentile15
Maximum15
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.7027638
Coefficient of variation (CV)0.60686044
Kurtosis-1.2462245
Mean7.7493333
Median Absolute Deviation (MAD)4
Skewness-0.063575923
Sum5812
Variance22.115988
MonotonicityNot monotonic
2024-11-25T18:46:52.915978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
5 59
 
7.9%
12 58
 
7.7%
14 56
 
7.5%
15 55
 
7.3%
2 52
 
6.9%
0 50
 
6.7%
10 50
 
6.7%
3 47
 
6.3%
9 47
 
6.3%
13 44
 
5.9%
Other values (6) 232
30.9%
ValueCountFrequency (%)
0 50
6.7%
1 36
4.8%
2 52
6.9%
3 47
6.3%
4 33
4.4%
5 59
7.9%
6 38
5.1%
7 44
5.9%
8 41
5.5%
9 47
6.3%
ValueCountFrequency (%)
15 55
7.3%
14 56
7.5%
13 44
5.9%
12 58
7.7%
11 40
5.3%
10 50
6.7%
9 47
6.3%
8 41
5.5%
7 44
5.9%
6 38
5.1%
Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2
157 
5
155 
3
150 
1
150 
4
138 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters750
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row5
4th row5
5th row3

Common Values

ValueCountFrequency (%)
2 157
20.9%
5 155
20.7%
3 150
20.0%
1 150
20.0%
4 138
18.4%

Length

2024-11-25T18:46:53.054504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T18:46:53.181781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 157
20.9%
5 155
20.7%
3 150
20.0%
1 150
20.0%
4 138
18.4%

Most occurring characters

ValueCountFrequency (%)
2 157
20.9%
5 155
20.7%
3 150
20.0%
1 150
20.0%
4 138
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 157
20.9%
5 155
20.7%
3 150
20.0%
1 150
20.0%
4 138
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 157
20.9%
5 155
20.7%
3 150
20.0%
1 150
20.0%
4 138
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 157
20.9%
5 155
20.7%
3 150
20.0%
1 150
20.0%
4 138
18.4%

DistanceFromCompany
Real number (ℝ)

Unique 

Distinct750
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.749134
Minimum1.1038259
Maximum50.992462
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-11-25T18:46:53.325398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.1038259
5-th percentile3.3994082
Q112.7935
median25.780826
Q338.317989
95-th percentile48.740592
Maximum50.992462
Range49.888636
Interquartile range (IQR)25.524489

Descriptive statistics

Standard deviation14.738232
Coefficient of variation (CV)0.57237778
Kurtosis-1.2457893
Mean25.749134
Median Absolute Deviation (MAD)12.773817
Skewness0.032425012
Sum19311.851
Variance217.21549
MonotonicityNot monotonic
2024-11-25T18:46:53.476858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.18675168 1
 
0.1%
18.53888115 1
 
0.1%
22.83789089 1
 
0.1%
25.69895746 1
 
0.1%
32.27390302 1
 
0.1%
15.58467254 1
 
0.1%
6.047081278 1
 
0.1%
40.59678115 1
 
0.1%
21.72170235 1
 
0.1%
33.50566117 1
 
0.1%
Other values (740) 740
98.7%
ValueCountFrequency (%)
1.10382588 1
0.1%
1.135407491 1
0.1%
1.185120342 1
0.1%
1.339827429 1
0.1%
1.369595072 1
0.1%
1.438860597 1
0.1%
1.500520528 1
0.1%
1.595899149 1
0.1%
1.675682115 1
0.1%
1.732356364 1
0.1%
ValueCountFrequency (%)
50.99246222 1
0.1%
50.966814 1
0.1%
50.95247491 1
0.1%
50.91593783 1
0.1%
50.90999538 1
0.1%
50.87590656 1
0.1%
50.84045903 1
0.1%
50.73874773 1
0.1%
50.67890864 1
0.1%
50.66325982 1
0.1%

InterviewScore
Real number (ℝ)

Distinct100
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.176
Minimum0
Maximum100
Zeros4
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-11-25T18:46:53.625634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.45
Q127
median55
Q376
95-th percentile95
Maximum100
Range100
Interquartile range (IQR)49

Descriptive statistics

Standard deviation28.527006
Coefficient of variation (CV)0.54674575
Kurtosis-1.2020268
Mean52.176
Median Absolute Deviation (MAD)25
Skewness-0.1175798
Sum39132
Variance813.79008
MonotonicityNot monotonic
2024-11-25T18:46:53.779193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 15
 
2.0%
74 15
 
2.0%
9 14
 
1.9%
70 14
 
1.9%
76 13
 
1.7%
75 13
 
1.7%
29 13
 
1.7%
85 12
 
1.6%
65 12
 
1.6%
57 12
 
1.6%
Other values (90) 617
82.3%
ValueCountFrequency (%)
0 4
 
0.5%
1 10
1.3%
2 6
0.8%
3 4
 
0.5%
4 6
0.8%
5 6
0.8%
7 2
 
0.3%
8 7
0.9%
9 14
1.9%
10 6
0.8%
ValueCountFrequency (%)
100 5
0.7%
99 9
1.2%
98 5
0.7%
97 7
0.9%
96 10
1.3%
95 6
0.8%
94 5
0.7%
93 8
1.1%
92 6
0.8%
91 7
0.9%

SkillScore
Real number (ℝ)

Zeros 

Distinct101
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.742667
Minimum0
Maximum100
Zeros8
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-11-25T18:46:53.927579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q126
median54
Q378
95-th percentile96
Maximum100
Range100
Interquartile range (IQR)52

Descriptive statistics

Standard deviation29.347586
Coefficient of variation (CV)0.56718349
Kurtosis-1.1953707
Mean51.742667
Median Absolute Deviation (MAD)25
Skewness-0.10282705
Sum38807
Variance861.28082
MonotonicityNot monotonic
2024-11-25T18:46:54.085403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79 19
 
2.5%
96 15
 
2.0%
2 14
 
1.9%
18 14
 
1.9%
88 13
 
1.7%
36 12
 
1.6%
67 12
 
1.6%
55 12
 
1.6%
76 11
 
1.5%
89 11
 
1.5%
Other values (91) 617
82.3%
ValueCountFrequency (%)
0 8
1.1%
1 8
1.1%
2 14
1.9%
3 6
0.8%
4 4
 
0.5%
5 4
 
0.5%
6 6
0.8%
7 5
 
0.7%
8 8
1.1%
9 7
0.9%
ValueCountFrequency (%)
100 10
1.3%
99 7
0.9%
98 9
1.2%
97 3
 
0.4%
96 15
2.0%
95 5
 
0.7%
94 9
1.2%
93 3
 
0.4%
92 10
1.3%
91 5
 
0.7%

PersonalityScore
Real number (ℝ)

Distinct101
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.332
Minimum0
Maximum100
Zeros7
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-11-25T18:46:54.244671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q122
median49
Q376
95-th percentile96
Maximum100
Range100
Interquartile range (IQR)54

Descriptive statistics

Standard deviation29.839722
Coefficient of variation (CV)0.60487557
Kurtosis-1.26486
Mean49.332
Median Absolute Deviation (MAD)27
Skewness0.030957147
Sum36999
Variance890.40899
MonotonicityNot monotonic
2024-11-25T18:46:54.413738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 14
 
1.9%
20 14
 
1.9%
12 13
 
1.7%
18 13
 
1.7%
14 12
 
1.6%
46 12
 
1.6%
77 12
 
1.6%
11 11
 
1.5%
52 11
 
1.5%
27 11
 
1.5%
Other values (91) 627
83.6%
ValueCountFrequency (%)
0 7
0.9%
1 8
1.1%
2 8
1.1%
3 7
0.9%
4 6
0.8%
5 8
1.1%
6 9
1.2%
7 10
1.3%
8 7
0.9%
9 7
0.9%
ValueCountFrequency (%)
100 7
0.9%
99 9
1.2%
98 9
1.2%
97 4
 
0.5%
96 10
1.3%
95 9
1.2%
94 5
0.7%
93 9
1.2%
92 10
1.3%
91 8
1.1%

RecruitmentStrategy
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
2
396 
1
217 
3
137 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters750
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 396
52.8%
1 217
28.9%
3 137
 
18.3%

Length

2024-11-25T18:46:54.564027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T18:46:54.683499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 396
52.8%
1 217
28.9%
3 137
 
18.3%

Most occurring characters

ValueCountFrequency (%)
2 396
52.8%
1 217
28.9%
3 137
 
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 396
52.8%
1 217
28.9%
3 137
 
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 396
52.8%
1 217
28.9%
3 137
 
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 396
52.8%
1 217
28.9%
3 137
 
18.3%

HiringDecision
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
0
517 
1
233 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters750
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 517
68.9%
1 233
31.1%

Length

2024-11-25T18:46:54.809152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-25T18:46:54.929183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 517
68.9%
1 233
31.1%

Most occurring characters

ValueCountFrequency (%)
0 517
68.9%
1 233
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 517
68.9%
1 233
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 517
68.9%
1 233
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 517
68.9%
1 233
31.1%

Interactions

2024-11-25T18:46:50.750849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:47.401256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:48.043658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:48.756630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:49.419234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:50.073086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:50.862325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:47.518579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:48.159881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:48.870795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:49.524893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:50.185272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:50.983409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:47.629105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:48.287359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:48.989447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:49.630373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:50.318306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:51.093752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:47.731318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:48.412485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:49.098108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:49.734490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:50.428200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:51.204369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:47.837610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:48.529868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:49.207737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:49.832723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:50.541920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:51.462539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:47.941150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:48.643817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:49.318507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:49.953879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-25T18:46:50.641048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-25T18:46:55.028802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeDistanceFromCompanyEducationLevelExperienceYearsGenderHiringDecisionInterviewScorePersonalityScorePreviousCompaniesRecruitmentStrategySkillScore
Age1.000-0.0380.0330.0430.0000.000-0.0700.0500.0700.0000.002
DistanceFromCompany-0.0381.0000.0790.0170.0350.000-0.024-0.0070.0000.000-0.024
EducationLevel0.0330.0791.0000.0000.0000.2230.0000.0000.0000.0000.063
ExperienceYears0.0430.0170.0001.0000.1280.135-0.0370.0220.0650.0420.040
Gender0.0000.0350.0000.1281.0000.0000.0700.0000.0320.0000.000
HiringDecision0.0000.0000.2230.1350.0001.0000.2150.2270.0000.5670.224
InterviewScore-0.070-0.0240.000-0.0370.0700.2151.000-0.0260.0000.000-0.019
PersonalityScore0.050-0.0070.0000.0220.0000.227-0.0261.0000.0000.0470.028
PreviousCompanies0.0700.0000.0000.0650.0320.0000.0000.0001.0000.0000.000
RecruitmentStrategy0.0000.0000.0000.0420.0000.5670.0000.0470.0001.0000.000
SkillScore0.002-0.0240.0630.0400.0000.224-0.0190.0280.0000.0001.000

Missing values

2024-11-25T18:46:51.605433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-25T18:46:51.806918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeGenderEducationLevelExperienceYearsPreviousCompaniesDistanceFromCompanyInterviewScoreSkillScorePersonalityScoreRecruitmentStrategyHiringDecision
0251214450.18675290799021
145025223.66337357566030
244016512.84247496786921
328041353.6453721165930
4330312335.18941064912120
547115345.90732245557020
647120214.26042725961310
740128115.32368799589821
84803519.66980370889820
938015141.65738318594120
AgeGenderEducationLevelExperienceYearsPreviousCompaniesDistanceFromCompanyInterviewScoreSkillScorePersonalityScoreRecruitmentStrategyHiringDecision
74049025248.69726140117020
74136024420.31840764118210
742330211346.93736564431430
743460311311.49590261855130
744310210121.1186532624620
74549129237.0566187442830
74642128317.4615318171411
74728145515.39398470155820
74844122347.8819898665420
74927120338.50766164761310